Precision medicine marks a transformative shift towards a patient-centric treatment approach, aiming to match ‘the right patients with the right drugs at the right time’. The exponential growth of data from diverse omics modalities, electronic health records, and medical imaging has created unprecedented opportunities for precision medicine. This explosion of data requires advanced processing and analytical tools. At the forefront of this revolution is artificial intelligence (AI), which excels at uncovering hidden patterns within these high-dimensional and complex datasets. AI facilitates the integration and analysis of diverse data types, unlocking unparalleled potential to characterise complex diseases, improve prognosis, and predict treatment response. Despite the enormous potential of AI, challenges related to interpretability, reliability, generalisability, and ethical considerations emerge when translating these tools from research settings into clinical practice.
Martínez et al. (Wed,) studied this question.
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